Biometrics is a technology for recognizing physical features, such as a fingerprint, a face, an iris, a vein and the like, which are different from person to person. Such physical features cannot be stolen or copied by others, like a key or password, and may be utilized in the security field or the like since they are not at risk of being changed or lost. Face recognition is a type of biometric technology that includes a technique for detecting a face region in a video or a picture image and identifying a face included in the detected face region. Such face recognition technology can be utilized in not only the security field but also a variety of other applications, in line with the progress in the era of smart phones.
Specifically, face recognition is a technique for identifying a face in a detected facial image by using positions of feature points. The feature points may include a center point of an eye, both end points of each eye, both end points and a center point of an eyebrow, both end points of a lip or the like.
Although statistical methods, such as principal component analysis (PCA), local descriptor analysis (LDA) and the like, have traditionally been used in face recognition techniques, since feature information extracted from a local region of a face is more reliable for detecting a change in a facial image due to, for example, a change in pose or illumination, LDA is more commonly used in recent days.
Another technique, scale invariant feature transformation (SIFT) shows excellent recognition performance in object recognition, but does not show good performance in face recognition, and has limitations in its application. There are various reasons why this method shows poor performance in face recognition. First, since a facial image (unlike an image of an object) is not angulated but deformable and even a face of the same person looks quite different depending on viewing directions, a position of a feature point also changes accordingly. However, the SIFT method cannot take such a change into account to recognize a face. Second, a distance-based matching technique or a region-based matching technique generally used in a descriptor-based matching shows poor performance in the case of a facial image taken under a strong illumination or an image of a person in an unusual pose. Accordingly, the above-described problems should be solved in order to apply the SIFT (which is normally used for object recognition) to face recognition.
Even when face recognition is performed by a technique other than the SIFT (such as, a SURF, LESH, or GLOH), it is difficult to improve the performance of face recognition.